PatrickHaller
commited on
Commit
•
f57435d
1
Parent(s):
33340e5
Upload configuration_xlstm.py with huggingface_hub
Browse files- configuration_xlstm.py +97 -0
configuration_xlstm.py
ADDED
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import json
|
2 |
+
from typing import Any, Dict, Optional
|
3 |
+
|
4 |
+
from dacite import Config as DaciteConfig
|
5 |
+
from dacite import from_dict
|
6 |
+
from omegaconf import OmegaConf
|
7 |
+
from transformers.configuration_utils import PretrainedConfig
|
8 |
+
from xlstm import xLSTMLMModelConfig
|
9 |
+
|
10 |
+
# from .config_presets import xlstm_cfg_map
|
11 |
+
|
12 |
+
|
13 |
+
class xLSTMConfig(PretrainedConfig):
|
14 |
+
"""XLSTM configuration class.
|
15 |
+
We seperate the specific xLSTM model configuration
|
16 |
+
from the rest due to the heavy nesting of the configuration.
|
17 |
+
"""
|
18 |
+
|
19 |
+
model_type = "xlstm"
|
20 |
+
|
21 |
+
def __init__(
|
22 |
+
self, vocab_size: int = 32000, config: Optional[Dict[str, Any]] = None, **kwargs
|
23 |
+
):
|
24 |
+
super().__init__(**kwargs)
|
25 |
+
|
26 |
+
cfg = OmegaConf.create(config)
|
27 |
+
cfg["vocab_size"] = vocab_size
|
28 |
+
for key, value in kwargs.items():
|
29 |
+
cfg[key] = value
|
30 |
+
|
31 |
+
self._xlstm_config = cfg
|
32 |
+
self.vocab_size = vocab_size
|
33 |
+
self.embedding_dim = cfg.get("embedding_dim")
|
34 |
+
self.context_length = cfg.get("context_length")
|
35 |
+
|
36 |
+
def to_xlstm_config(self):
|
37 |
+
return from_dict(
|
38 |
+
data_class=xLSTMLMModelConfig,
|
39 |
+
data=OmegaConf.to_container(self._xlstm_config),
|
40 |
+
config=DaciteConfig(strict=True),
|
41 |
+
)
|
42 |
+
|
43 |
+
def to_dict(self) -> Dict[str, Any]:
|
44 |
+
"""
|
45 |
+
Converts the configuration to a dictionary for serialization.
|
46 |
+
"""
|
47 |
+
output = super().to_dict()
|
48 |
+
output["_xlstm_config"] = OmegaConf.to_container(
|
49 |
+
self._xlstm_config, resolve=True
|
50 |
+
)
|
51 |
+
relevant_keys = [
|
52 |
+
"vocab_size",
|
53 |
+
"embedding_dim",
|
54 |
+
"context_length",
|
55 |
+
"torch_dtype",
|
56 |
+
"_xlstm_config",
|
57 |
+
"transformers_version",
|
58 |
+
"architectures",
|
59 |
+
"model_type",
|
60 |
+
]
|
61 |
+
output_ = output.copy()
|
62 |
+
for key in output.keys():
|
63 |
+
if key not in relevant_keys:
|
64 |
+
output_.pop(key)
|
65 |
+
return output_
|
66 |
+
|
67 |
+
@classmethod
|
68 |
+
def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
|
69 |
+
"""
|
70 |
+
Creates a configuration instance from a dictionary.
|
71 |
+
"""
|
72 |
+
xlstm_config = config_dict.pop("_xlstm_config")
|
73 |
+
vocab_size = config_dict.pop("vocab_size")
|
74 |
+
config = cls(vocab_size=vocab_size, config=xlstm_config)
|
75 |
+
if "auto_map" in config_dict and config_dict["auto_map"]:
|
76 |
+
setattr(config, "auto_map", config_dict.pop("auto_map"))
|
77 |
+
|
78 |
+
# breakpoint()
|
79 |
+
# config.xlstm_config = xlstm_config
|
80 |
+
if "return_unused_kwargs" in kwargs and kwargs["return_unused_kwargs"]:
|
81 |
+
return config, {}
|
82 |
+
|
83 |
+
return config
|
84 |
+
|
85 |
+
def to_json_string(self, *args, **kwargs) -> str:
|
86 |
+
"""
|
87 |
+
Serializes the instance to a JSON string.
|
88 |
+
"""
|
89 |
+
return json.dumps(self.to_dict(), indent=2)
|
90 |
+
|
91 |
+
@classmethod
|
92 |
+
def from_json_string(cls, json_string: str):
|
93 |
+
"""
|
94 |
+
Deserializes the instance from a JSON string.
|
95 |
+
"""
|
96 |
+
config_dict = json.loads(json_string)
|
97 |
+
return cls.from_dict(config_dict)
|